With the rapid development of wireless data service, people have increasing demands for spectrum resources and the spectrum resources are gradually decreased. On the other hand, some frequency bands utilization is very low. This leads to low efficiency of the traditional pre-assigned static spectrum management mode which is authorized to use for a long time. Therefore, how to flexibly and efficiently use the spectrum resources becomes a hotspot issue. J. Mitola first proposed the cognitive radio based spectrum-sharing concept, and such technology enables flexible utilization of the spectrum resources at any time and any place. The emergence of the cognitive radio technology greatly improves the spectrum utilization ratio, and alleviates the contradiction between the increasing demands for wireless service and the gradually decreasing spectrum resources, and the technology is commonly considered as the optimal solution for solving the existing problem of low utilization ratio of the radio spectrum.
In the cognitive radio system, the first problem to be solved is how to judge whether a signal of an authorized user exists on the spectrum. This problem is known as spectrum detecting or spectrum sensing. A common spectrum detecting method includes: energy detection, matching filter detection and cyclostationary detection. The energy detection has low complexity, but it is affected by uncertainty of the noise and its performance deteriorates severely. The matching filter has superior performance, but it needs to know the characteristic of signal being sent. Cyclostationary detection also has superior performance, but it has high complexity, and has certain limitation on practical application.
According to the covariance matrix of a receipt signal, it can judge whether a signal or noise exists on the frequency band, and the theoretical foundation of work is as follows. Generally, it is known that when a signal exists, the covariance matrix of the signal is not a diagonal matrix, and when only noise exists, the covariance matrix of the receipt signal is a matrix with equal diagonal elements. Based on the theoretical foundation, the traditional technical solution provides a method for spectrum sensing by using characteristic values of the covariance matrix of the receipt signal, a judgment variable may consist of the characteristic values of the covariance matrix, and provides a method for constructing the judgment variable, i.e., ratio of maximum characteristic values to minimum characteristic values. It can be seen that, ideally, when only the noise exists, the ratio of the maximum characteristic values to minimum characteristic values is 1, and when the signal exists, the ratio is greater than 1. The method is advantaged in no need of sending any prior information of signal and no need of any statistical property of the noise. However, calculating the maximum and minimum characteristic values needs complex characteristic value decomposition, and the computation complexity is O(L3), wherein L is the dimension of the covariance matrix. Therefore, the method has extremely high complexity, and especially when L is large, it is difficult to implement.